- Support for GraphFrames which aims to provide the functionality of GraphX.
- Perform graph algorithms like: PageRank, ShortestPaths and many others.
- Designed to work with sparklyr and the sparklyr extensions.
For those already using sparklyr simply run:
install.packages("graphframes")
# or, for the development version,
# devtools::install_github("rstudio/graphframes")Otherwise, install first sparklyr from CRAN using:
install.packages("sparklyr")The examples make use of the highschool dataset from the ggplot
package.
We will calculate PageRank over the built-in “friends” dataset as follows.
library(graphframes)
library(sparklyr)
library(dplyr)
# connect to spark using sparklyr
sc <- spark_connect(master = "local", version = "2.3.0")
# obtain the example graph
g <- gf_friends(sc)
# compute PageRank
results <- gf_pagerank(g, tol = 0.01, reset_probability = 0.15)
results## GraphFrame
## Vertices:
## $ id <chr> "f", "b", "g", "a", "d", "c", "e"
## $ name <chr> "Fanny", "Bob", "Gabby", "Alice", "David", "Charlie",...
## $ age <int> 36, 36, 60, 34, 29, 30, 32
## $ pagerank <dbl> 0.3283607, 2.6555078, 0.1799821, 0.4491063, 0.3283607...
## Edges:
## $ src <chr> "b", "c", "d", "e", "a", "a", "e", "f"
## $ dst <chr> "c", "b", "a", "f", "e", "b", "d", "c"
## $ relationship <chr> "follow", "follow", "friend", "follow", "friend",...
## $ weight <dbl> 1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.5, 1.0
We can then visualize the results by collecting the results to R:
library(tidygraph)
library(ggraph)
vertices <- results %>%
gf_vertices() %>%
collect()
edges <- results %>%
gf_edges() %>%
collect()
edges %>%
as_tbl_graph() %>%
activate(nodes) %>%
left_join(vertices, by = c(name = "id")) %>%
ggraph(layout = "nicely") +
geom_node_label(aes(label = name.y, color = pagerank)) +
geom_edge_link(
aes(
alpha = weight,
start_cap = label_rect(node1.name.y),
end_cap = label_rect(node2.name.y)
),
arrow = arrow(length = unit(4, "mm"))
) +
theme_graph(fg_text_colour = 'white')
Appart from calculating PageRank using gf_pagerank, many other
functions are available, including:
gf_bfs(): Breadth-first search (BFS).gf_connected_components(): Connected components.gf_shortest_paths(): Shortest paths algorithm.gf_scc(): Strongly connected components.gf_triangle_count(): Computes the number of triangles passing through each vertex and others.gf_degrees(): Degrees of vertices
For instance, one can calculate the degrees of vertices using
gf_degrees as follows:
gf_friends(sc) %>% gf_degrees()## # Source: spark<?> [?? x 2]
## id degree
## * <chr> <int>
## 1 f 2
## 2 b 3
## 3 a 3
## 4 c 3
## 5 e 3
## 6 d 2
Finally, we disconnect from Spark:
spark_disconnect(sc)
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